Background & Context

The Thera bank recently saw a steep decline in the number of users of their credit card, credit cards are a good source of income for banks because of different kinds of fees charged by the banks like annual fees, balance transfer fees, and cash advance fees, late payment fees, foreign transaction fees, and others. Some fees are charged to every user irrespective of usage, while others are charged under specified circumstances.

Customers’ leaving credit cards services would lead bank to loss, so the bank wants to analyze the data of customers and identify the customers who will leave their credit card services and reason for same – so that bank could improve upon those areas

You as a Data scientist at Thera bank need to come up with a classification model that will help the bank improve their services so that customers do not renounce their credit cards

Objective

Data Dictionary:

Index

Overview of the dataset

Observations-

Checking data types and number of non-null values for each column.

Observations-

Observations-

Number of unique values in each column

Observations-

Summary of the dataset

Observations-

Number of observations in each category

Observations-

EDA

Uni-variate analysis of numerical variables

Uni-variate analysis of categorical features

Bivariate Analysis

Split the dataset/Data Pre-processing

Processing data

Splitting the data into train and test sets¶

One Hot Encoding

Model building - Logistic Regression, Bagging, RandomForest, GradientBoosting, AdaBoost, Xgboost, Decision_tree

AdaBoost

RandomForest

XGBoost

AdaBoost

RandomForest

XGBoost

Model Performances

Business Recommendations/Actionable Insights

These are the main feature we can use to decide if the customer will renounce their credit cards:

 1) Total_Trans_Amt          
 2) Total_Ct_Chng_Q4_Q1       
 3) Avg_Utilization_Ratio        
 4) Total_Relationship_Count             
 5) Total_Amt_Chng_Q4_Q1